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The Physical Mechanical Processes that Shape Tissues in the Early Embryo

  • Lance A. Davidson
Chapter
Part of the Studies in Mechanobiology, Tissue Engineering and Biomaterials book series (SMTEB, volume 4)

Abstract

The morphology of a multicellular organism and its internal organs is determined by interactions between an organism’s genome and the physical properties of living matter. Recent successes in sequencing the genome have revived interest the generation of physical shape, or morphogenesis, the physical properties of living matter, and how biological and biophysical processes shape that living matter during development. One of the goals of modern developmental biology is to understand how tissues are shaped and how physiological function is initiated. Remarkable advances in cell and molecular biology have led to a wealth of data on the molecular mechanisms required during early development. More recently, developmental biologists have been turning to biophysical and bioengineering approaches to understand how embryos as well as organs are shaped by these molecular mechanisms. These studies are finding hints that mechanical processes may be playing novel roles in developing embryos in addition to their direct roles in shaping tissues. Many early molecular pathways regulating cell differentiation and embryonic morphogenesis are reused as tumors grow and metastasize and during the regeneration of injured or damaged tissues. These discoveries have attracted cancer biologists and tissue engineers to join developmental biologists in studying the mechanical processes that drive morphogenesis. In this review we will present a short primer for the engineer on developmental biology and embryonic morphogenesis and then describe experimental and theoretical approaches to investigate the physical principles of morphogenesis.

Keywords

Planar Cell Polarity Epithelial Sheet Tissue Movement Cell Shape Change Morphogenetic Movement 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The work was supported by grants from the NSF (IOS-0845775) and the NIH (HD044750).

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© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.Departments of Bioengineering and Developmental BiologyUniversity of PittsburghPittsburghUSA

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